ML Anthology

DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting

Alexander Rodríguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari, B. Aditya Prakash
AAAI 2021 pp. 15393-15400
doi:10.1609/AAAI.V35I17.17808 /aaai/2021/rodriguez2021aaai-deepcovid/

Abstract

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCOVID, an operational deep learning framework designed for real-time COVID-19 forecasting. DeepCOVID works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

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Cite

Text

Rodríguez et al. "DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17808

Markdown

[Rodríguez et al. "DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/rodriguez2021aaai-deepcovid/) doi:10.1609/AAAI.V35I17.17808

BibTeX

@inproceedings{rodriguez2021aaai-deepcovid,
  title     = {{DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting}},
  author    = {Rodríguez, Alexander and Tabassum, Anika and Cui, Jiaming and Xie, Jiajia and Ho, Javen and Agarwal, Pulak and Adhikari, Bijaya and Prakash, B. Aditya},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15393-15400},
  doi       = {10.1609/AAAI.V35I17.17808},
  url       = {https://mlanthology.org/aaai/2021/rodriguez2021aaai-deepcovid/}
}